epicompare is now available via DockerHub as a containerised environment with Rstudio and all necessary dependencies pre-installed.
First, install Docker if you have not already.
Create an image of the Docker container in command line:
docker pull neurogenomicslab/epicompare
Once the image has been created, you can launch it with:
docker run \
-d \
-e ROOT=true \
-e PASSWORD="<your_password>" \
-v ~/Desktop:/Desktop \
-v /Volumes:/Volumes \
-p 8787:8787 \
neurogenomicslab/epicompare
<your_password>
above with whatever you want your password to be.-v
flags for your particular use case.-d
ensures the container will run in “detached” mode,
which means it will persist even after you’ve closed your command line session.If you are using a system that does not allow Docker (as is the case for many institutional computing clusters), you can instead install Docker images via Singularity.
singularity pull docker://neurogenomicslab/epicompare
Finally, launch the containerised Rstudio by entering the following URL in any web browser: http://localhost:8787/
Login using the credentials set during the Installation steps.
utils::sessionInfo()
## R Under development (unstable) (2024-03-18 r86148)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] EpiCompare_1.7.5 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3
## [2] jsonlite_1.8.8
## [3] magrittr_2.0.3
## [4] GenomicFeatures_1.55.4
## [5] farver_2.1.1
## [6] rmarkdown_2.26
## [7] fs_1.6.3
## [8] BiocIO_1.13.0
## [9] zlibbioc_1.49.3
## [10] vctrs_0.6.5
## [11] memoise_2.0.1
## [12] Rsamtools_2.19.4
## [13] RCurl_1.98-1.14
## [14] ggtree_3.11.1
## [15] htmltools_0.5.7
## [16] S4Arrays_1.3.6
## [17] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [18] plotrix_3.8-4
## [19] AnnotationHub_3.11.3
## [20] curl_5.2.1
## [21] SparseArray_1.3.4
## [22] gridGraphics_0.5-1
## [23] sass_0.4.9
## [24] KernSmooth_2.23-22
## [25] bslib_0.6.1
## [26] htmlwidgets_1.6.4
## [27] plyr_1.8.9
## [28] plotly_4.10.4
## [29] impute_1.77.0
## [30] cachem_1.0.8
## [31] GenomicAlignments_1.39.4
## [32] igraph_2.0.3
## [33] downloadthis_0.3.3
## [34] lifecycle_1.0.4
## [35] pkgconfig_2.0.3
## [36] Matrix_1.6-5
## [37] R6_2.5.1
## [38] fastmap_1.1.1
## [39] GenomeInfoDbData_1.2.11
## [40] MatrixGenerics_1.15.0
## [41] digest_0.6.35
## [42] aplot_0.2.2
## [43] enrichplot_1.23.1
## [44] colorspace_2.1-0
## [45] patchwork_1.2.0
## [46] AnnotationDbi_1.65.2
## [47] S4Vectors_0.41.4
## [48] DESeq2_1.43.4
## [49] GenomicRanges_1.55.4
## [50] RSQLite_2.3.5
## [51] filelock_1.0.3
## [52] fansi_1.0.6
## [53] httr_1.4.7
## [54] polyclip_1.10-6
## [55] abind_1.4-5
## [56] compiler_4.4.0
## [57] bit64_4.0.5
## [58] withr_3.0.0
## [59] BiocParallel_1.37.1
## [60] viridis_0.6.5
## [61] DBI_1.2.2
## [62] gplots_3.1.3.1
## [63] ggforce_0.4.2
## [64] MASS_7.3-60.2
## [65] ChIPseeker_1.39.0
## [66] rappdirs_0.3.3
## [67] DelayedArray_0.29.9
## [68] rjson_0.2.21
## [69] HDO.db_0.99.1
## [70] caTools_1.18.2
## [71] gtools_3.9.5
## [72] tools_4.4.0
## [73] scatterpie_0.2.1
## [74] ape_5.7-1
## [75] glue_1.7.0
## [76] restfulr_0.0.15
## [77] nlme_3.1-164
## [78] GOSemSim_2.29.1
## [79] shadowtext_0.1.3
## [80] grid_4.4.0
## [81] gridBase_0.4-7
## [82] reshape2_1.4.4
## [83] fgsea_1.29.0
## [84] generics_0.1.3
## [85] BSgenome_1.71.2
## [86] gtable_0.3.4
## [87] tzdb_0.4.0
## [88] seqPattern_1.35.0
## [89] tidyr_1.3.1
## [90] hms_1.1.3
## [91] data.table_1.15.2
## [92] tidygraph_1.3.1
## [93] utf8_1.2.4
## [94] XVector_0.43.1
## [95] BiocGenerics_0.49.1
## [96] stringr_1.5.1
## [97] ggrepel_0.9.5
## [98] BiocVersion_3.19.1
## [99] pillar_1.9.0
## [100] yulab.utils_0.1.4
## [101] splines_4.4.0
## [102] dplyr_1.1.4
## [103] tweenr_2.0.3
## [104] treeio_1.27.0
## [105] BiocFileCache_2.11.1
## [106] lattice_0.22-5
## [107] rtracklayer_1.63.1
## [108] bit_4.0.5
## [109] tidyselect_1.2.1
## [110] GO.db_3.18.0
## [111] locfit_1.5-9.9
## [112] Biostrings_2.71.4
## [113] knitr_1.45
## [114] gridExtra_2.3
## [115] bookdown_0.38
## [116] BRGenomics_1.15.1
## [117] IRanges_2.37.1
## [118] SummarizedExperiment_1.33.3
## [119] stats4_4.4.0
## [120] xfun_0.42
## [121] graphlayouts_1.1.1
## [122] Biobase_2.63.0
## [123] matrixStats_1.2.0
## [124] stringi_1.8.3
## [125] lazyeval_0.2.2
## [126] ggfun_0.1.4
## [127] yaml_2.3.8
## [128] boot_1.3-30
## [129] evaluate_0.23
## [130] codetools_0.2-19
## [131] ggraph_2.2.1
## [132] qvalue_2.35.0
## [133] tibble_3.2.1
## [134] BiocManager_1.30.22
## [135] ggplotify_0.1.2
## [136] cli_3.6.2
## [137] munsell_0.5.0
## [138] jquerylib_0.1.4
## [139] Rcpp_1.0.12
## [140] GenomeInfoDb_1.39.9
## [141] dbplyr_2.5.0
## [142] png_0.1-8
## [143] XML_3.99-0.16.1
## [144] parallel_4.4.0
## [145] readr_2.1.5
## [146] ggplot2_3.5.0
## [147] blob_1.2.4
## [148] DOSE_3.29.2
## [149] bitops_1.0-7
## [150] tidytree_0.4.6
## [151] viridisLite_0.4.2
## [152] scales_1.3.0
## [153] genomation_1.35.0
## [154] purrr_1.0.2
## [155] crayon_1.5.2
## [156] rlang_1.1.3
## [157] fastmatch_1.1-4
## [158] cowplot_1.1.3
## [159] KEGGREST_1.43.0